Eliciting properties of probability distributions
Eliciting properties of probability distributions
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DOI：
10.1145/1386790.1386813
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发表时间：
2008-07
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影响因子：
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通讯作者：
Nicolas S. Lambert;David M. Pennock;Y. Shoham
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文献类型：
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作者：
Nicolas S. Lambert;David M. Pennock;Y. Shoham
我们调查的问题，如实地引出一个专家的概率分布的属性的评估，其中的属性是任何实值函数的分布，如均值或方差。我们表明，并不是所有的属性是elicitable的，例如，平均值是elicitable和方差不是。对于那些是elicitable，我们提供了一个表征所有支付（或“得分”）功能，诱导真实的启示。我们还考虑了属性集的启发。然后，我们观察到，属性总是可以推断出从集合的elicitable属性。这自然暗示了启发复杂性的概念;属性的启发复杂性是隐含该属性的集合的最小大小。最后，我们将讨论预测市场的应用。
We investigate the problem of truthfully eliciting an expert's assessment of a property of a probability distribution, where a property is any real-valued function of the distribution such as mean or variance. We show that not all properties are elicitable; for example, the mean is elicitable and the variance is not. For those that are elicitable, we provide a representation theorem characterizing all payment (or "score") functions that induce truthful revelation. We also consider the elicitation of sets of properties. We then observe that properties can always be inferred from sets of elicitable properties. This naturally suggests the concept of elicitation complexity; the elicitation complexity of property is the minimal size of such a set implying the property. Finally we discuss applications to prediction markets.